Digital Health Innovation and Informatics
SS 25 - Digital Health/Informatics 2
147 - Elevated Coronary Artery Calcium Quantified by a Deep Learning Model from Radiotherapy Planning Scans Predicts Mortality in Lung Cancer
Tuesday, September 17
3:45 PM - 3:55 PM
Location: Room W176
Katelyn Atkins, MD, PhD
Cedars-Sinai Medical Center
Brigham and Women's Hospital and Massachusetts General Hospital: Resident Physician: Employee
Elevated Coronary Artery Calcium Quantified by a Deep Learning Model from Radiotherapy Planning Scans Predicts Mortality in Lung Cancer
K. M. Atkins1, J. Weiss2, R. Zeleznik2, T. L. Chaunzwa3, U. Hoffmann4, H. Aerts5, and R. H. Mak6; 1Harvard Radiation Oncology Program, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, MA, 2Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Boston, MA, 3Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, MA, 4Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 5Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 6Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
Purpose/Objective(s): Coronary artery calcium (CAC) is one of the strongest predictors of long-term atherosclerotic coronary vascular disease in asymptomatic individuals, however the feasibility of quantitating this measurement from radiotherapy (RT) planning computed tomography (CT) scans is unknown. Since patients with non-small cell lung cancer (NSCLC) represent a distinctly high cardiovascular risk population, we sought to quantify CAC from RT planning CTs in NSCLC patients using a deep learning model.
Materials/Methods: Retrospective analysis of non-contrast enhanced RT planning CTs from 464 consecutive locally-advanced NSCLC patients treated with thoracic RT. The CAC algorithm was previously trained on 693 independent cardiac-gated CT scans manually segmented by expert readers using three consecutive deep learning networks for segmentation and tested on three independent cohorts of 441, 664, and 398 scans. Plaques ≥1 cubic millimeter were volumetrically measured and multiplied by a maximum plaque density factor to generate an Agatson-like CAC Score. The model was used to calculate a CAC risk group for each planning CT, defined as CAC=0 (very low risk) versus CAC>0 (elevated risk). For patient factors, continuous covariates were evaluated using a Wilcoxon rank sum test whereas categorical covariates were compared using a Fisher exact test. Univariable Cox regression analysis was performed and Kaplan-Meier estimates of all-cause mortality were calculated.
Results: After a median follow-up of 18 months, there were 353 deaths (2-year all-cause mortality, 52.2% [95% CI, 47.7-56.8%]). Of the 464 planning CTs, 35% (162/464) were CAC=0 and 65% (302/464) were CAC>0. Patients in the CAC>0 group were older (median age 67 vs. 60 years, P<.0001), more likely male (58% vs. 37%, P<.001), have an ever-smoking history (95% vs. 84%, P<.001), and less likely treated with surgery (32% vs. 45%, P=.003) or chemotherapy (91% vs. 97%, P=.008). There was no difference in mean heart dose delivered between CAC>0 vs. CAC=0 (11.9 Gy vs. 11.5 Gy, P=.83). CAC>0 was associated with an increased risk of all-cause mortality on univariable Cox regression (hazard ratio, 1.29 [95% CI, 1.03-1.62]; P=.027). The 2-year all-cause mortality stratified by CAC group was 56.7% (95% CI, 51.1-62.3%) in CAC>0 vs. 47.1% (95% CI, 39.6-55.1%) in CAC=0 (log-rank P=.0259).
Conclusion: Coronary artery calcium was effectively measured from non-contrast RT planning CTs using an automated deep learning model. Elevated CAC, as predicted by the deep neural network, was associated with an increased risk of all-cause mortality on univariable analysis in locally-advanced NSCLC patients despite a high competing risk of lung cancer death. Deeper investigation of contributing and confounding variables in a comprehensive predictive model is warranted.
Author Disclosure: K.M. Atkins: None. J. Weiss: None. T.L. Chaunzwa: None. U. Hoffmann: Research Grant; Medimmune, Kowa Inc, HeartFlow Inc. H. Aerts: Scientific Advisor; Genospace, Sphera. R.H. Mak: Honoraria; NewRT. Advisory Board; AstraZeneca. Travel Expenses; NewRT.